Only two things are clear when it comes to antidepressants: they’re big business, and big business makes everything else less clear.

With an annual market of nearly $10 billion in the USA alone, pharmaceutical companies want to make antidepressants look like they’re worth prescribing. Unsurprisingly, studies published by these companies are more likely to find their own drugs effective than independent research (Lexchin, 2003), and negative antidepressant trials tend to get buried (Eyding, 2010).

Meta-analyses are relatively cheap and easy to do. You don’t need to collect any new data, just statistically combine lots of pre-existing trial data. We already know that meta-analyses of general medical drugs written with pharmaceutical company influence tend to be of worse quality and to report more positive results (Jørgensen, 2008). But antidepressant meta-analyses have never been studied in this way, until now.

Antidepressants have an annual market of nearly $10 billion in the USA alone.

Methods

The research team searched MEDLINE for all meta-analyses of randomised controlled trials evaluating antidepressants in people with depressive conditions published since 2007. They looked for trials not just of all recognised antidepressants but also of drugs like quetiapine, which aren’t primarily antidepressants, but are approved for the purpose.

They accepted meta-analyses that compared antidepressants to placebo or other antidepressants, but not those that compared them to a non-drug treatment, like CBT. The main focus of the meta-analyses could be benefit, harm, treatment discontinuation or any combination of these, but not ‘predictors of outcomes’, whatever that means.

They identified whether:

The study had been sponsored (paid for) by a company who were producing one of the drugs being evaluated

Any of the authors had a conflict of interest, like working for a pharmaceutical company who made one of the drugs being studied or receiving money from them for another project

If they found a paper with no conflicts of interest declared by the authors, they checked three other recent papers by the same lead author to make sure they weren’t declaring anything elsewhere.

Then two of the team were blinded to which meta-analyses had biased authors, before judging whether the abstracts included any negative comments (‘drug X is not effective’). If there were no negative comments, they looked for positive statements. If positive statements were present, comments about the evidence being of low quality or inconclusive were not counted as negative. If there were no positive comments, those statements were taken as negative.

Results

The initial search found 1,111 papers. This was reduced to 259 by looking at abstracts, then another 74 were excluded by looking at full papers. This left 185 meta-analyses. They tended to:

Be from the US (35%) and Europe (33%)

Assess benefit as their primary focus (92%)

Evaluate patients with major depressive disorder only (82%)

Appear in specialist psychiatric journals (56%)

Study SSRIs (45%) and SNRIs (30%)

Compare antidepressants to placebo (74%)

Who paid for them?

46 (25%) by a company producing the drug being evaluated

20 (11%) by a not-for-profit organisation

33 (18%) by a government organisation

41 (22%) didn’t have funding

51 (28%) didn’t mention funding, but 11 (22%) of these had authors who were industry employees

How many authors were biased?

57 (31%) of studies had an author employed by a for-profit organisation, including 54 (29%) who had an author employed by the company producing the drug being evaluated.

134 (72%) of studies had an author with a conflict of interest with a for-profit organisation, including 111 (61%) who had an author receiving support from the producer of the drug being assessed.

This added up to 145 (78%) of studies with biased authors. 122 (66%) meta-analyses had authors employed by or receiving support from the producer of the drug under evaluation.

53 of the studies had no authors reporting a conflict of interest, but 13 of these had authors who’d declared them elsewhere.

How many trials had negative conclusions in their abstract?

127 (69%) had no negative conclusions

20 concluded the drugs didn’t work or worked worse than the comparator

20 concluded that the drug had poor safety or toxicity

21 said nothing positive and said the evidence was low quality, limited or inconclusive, which was counted as negative

Comparing industry-biased and non-industry-biased trials

Studies with industry-employed authors were less likely to:

Use a literature review (21% v 91%, p<0.001)

Use individual-level data (11% v 79%, p<0.001)

Use an active comparator (not placebo) (5% v 17%, p=0.04)

And industry-influenced trials gave biased results:

Those with an industry-employed author were 22 times less likely to report a negative conclusion (1/54 [2%] vs 57/131 [44%], p<0.001)

Studies were less likely to have negative conclusions if they were in specialist psychiatric journals (23/103, 23%) compared to general medical journals (9/15, 60%, p=0.004) and if they were published in the US (14/66, 21%) compared to elsewhere (44/119, 37%, p=0.03).

Even if the studies with industry-employed authors were removed from the analysis, studies were still less likely to have a negative conclusion if they had an author with a conflict of interest with the company producing the drug being evaluated (22/68 v 34/60, p=0.007).

Less than one third of meta-analyses had negative statements in the abstract conclusion.

Discussion

No one is surprised that the research funded by pharmaceutical companies appears to be biased. But they might be surprised to find that that bias has already crept so terminally into the realm of meta-analyses.

Nearly a third of antidepressant meta-analyses were authored by drug company employees. Only one of these had a negative conclusion, and even that only concerned side effects. Over another third had authors with less serious conflicts of interest and these reported less negative conclusions than independent studies too. Meta-analyses, the go-to resource of busy doctors around the world, seem to be the latest pharma marketing tool.

You could argue that basing how biased a study is on the conclusions in the abstract is superficial, but professionals only have time to read those very same conclusions.

To play devil’s advocate you might wonder if the authors of non-industry funded trials were also biased, hell-bent on proving that antidepressants are useless. Hardly likely though.

Implications for practice

If you want this paper’s results to improve your practice, you might want to:

Treat meta-analyses written by authors with conflicts of interest with extreme suspicion

Alex – I am surprised that people believe that meta-analyses are ‘objective’ or somehow less prone to bias than regular studies. Indeed, meta analyses are the ideal environment for bias to go un-noticed – meta analyses are a Trojan Horse in science.
Meta-analysis is in essence quite like conducting a regular study – it involves collecting data and inferential statistics and prone to the same Questionable Research Practices (QPRs). Unlike a regular study, however, meta-analytic authors ‘choose’ their own data. They have inclusion and exclusion criteria, but these are their ‘own’ criteria (however well justified or not).
Pharam are a very easy (and justifiable) target – in fact, sometimes I think they carry a big target just to help. But as I’m sure you would agree… the issues raised here about meta analyses (and many others) apply beyond pharma to other interventions including psychotherapy.
Meta analyses in healthcare are not the solution to our problems but a problem in need of a solution